IBM unveiled its next-generation Power Systems Servers incorporating its newly designed POWER9 processor. Built specifically for compute-intensive AI workloads, the new POWER9 systems are capable of improving the training times of deep learning frameworks by nearly 4x allowing enterprises to build more accurate AI applications, faster.
The new POWER9-based AC922 Power Systems are the first to embed PCI-Express 4.0, next-generation NVIDIA NVLink and OpenCAPI, which combined can accelerate data movement, calculated at 9.5x faster than PCI-E 3.0 based x86 systems.
The system was designed to drive demonstrable performance improvements across popular AI frameworks such as Chainer, TensorFlow and Caffe, as well as accelerated databases such as Kinetica.
As a result, data scientists can build applications faster, ranging from deep learning insights in scientific research, real-time fraud detection and credit risk analysis.
POWER9 is at the heart of the soon-to-be most powerful data-intensive supercomputers in the world, the U.S. Department of Energy’s “Summit” and “Sierra” supercomputers, and has been tapped by Google.
“Google is excited about IBM’s progress in the development of the latest POWER technology,” said Bart Sano, VP of Google Platforms “The POWER9 OpenCAPI Bus and large memory capabilities allow for further opportunities for innovation in Google data centers.”
Viswanath Ramaswamy, Director – Systems (India/South Asia) said, “The new IBM Power Systems Servers with POWER9 processor will be a game–changer for AI and deep learning workloads. The new processor delivers on unprecedented cognitive capabilities and can help Indian enterprises across all verticals to transform and up–scale on their AI and machine learning journey. The new server (AC22) improves training time of deep learning frameworks by 4x (vs x86), in turn delivering 10x faster performance bandwidth acceleration. This is the only platform with NVIDIA NVLink, PCI Express 4.0 and OpenCapi giving breakthrough acceleration for modern AI, high performance computing and accelerated database workloads.”
